Given the recent COVID-19 situation, many organizations and companies have asked their employees to work from home by connecting to their on-premises servers. This situation may continue a much more extended period in the future, thereby opening more threats to confidentiality and security to the information available in the organizations. It becomes of hell of a task for network administrators to counter the threats. Intrusion Detection Systems are deployed in firewalls to identify attacks or threats. In preset modern technologies, Network Intrusion Detection System plays a significant role in defense of the network threat. Statistical or pattern-based algorithms are used in NIDS to detect the benign activities that are taking place in the network. In this work, deep learning algorithms have developed in NIDS predictive models to detect anomalies and threats automatically. Performance of the proposed model assessed on the NSL-KDD dataset in the view of metrics such as accuracy, recall, precision, and F1-score. The experimental results show that the proposed deep learning model outperforms when compared with existing shallow models.
In this study, we propose an efficient method to identify unwanted growth in brain using SVM-PUK on convoluted textural features with reduced Gabor wavelet features. After preprocessing, GLCM features of image are extracted and further, convoluted with reduced Gabor features using PCA of the image. Then, the convoluted GLCM features and reduced Gabor features classified with the SVM using PUK kernel. The proposed method performance is evaluated on BRATS'18 database and achieved an accuracy of 91.31 % in recognizing the effected tissues, and shown better performance over ED, DTW, FFNN and PNN.
With the rapid growth of microblogs and online sites, an inordinate number of product reviews are available on the Internet. They not only help in analyzing, but also assist in making informed decisions about product quality. In the proposed work, an extended correlated principal component analysis (ECPCA) is used for dimensionality reduction. A comparative analysis is conducted on movie reviews (DB-1) and Twitter datasets (DB-2 and DB-3) in opinion mining extraction. The performance of naïve Bayes, CHIRP, and support vector machine (SVM) with kernel methods such as radial basis function (RBF), polynomial, and Pearson (PUK) are compared and analyzed on the three datasets. The experimental results using ECPCA for selecting relevant features and SVM-PUK as a classifier exhibit better performance on movie reviews and Twitter datasets. The performance of the proposed approach is 99.69%, 99.4%, and 99.54% on the DB-1, DB-2, and DB-3 datasets, respectively, and comparatively outperforms the existing methods.
In data centers, the energy-efficient scheduling of virtual machines (VMs) is critical to the full utilization of physical machines (PMs). Considering the sheer amount of data in cloud environment, this paper puts forward a novel energy-efficient scheduling method for VM consolidation and migration in cloud data centers. The proposed method optimizes the energy consumption at cloud data centers through three algorithms: the first algorithm describes the general migration of VMs among PMs; the second algorithm defines the migration of VMs among PMs; the third algorithm explains how the migration takes place. The effectiveness of our method was demonstrated on CloudSim with 5 PMs and 30 VMs, under the constraints of arrival time and deadline. The results show that our method can balance the load of input jobs and schedule the VMs properly, thus reducing the carbon emissions at the cloud data center.
Generative Adversarial Networks (GAN) generates model approaches using Convolution Neural Networks (CNN) to find out learning regularities and to discover the hidden patterns held in given input data. GAN is a generative model that is trained using two models such as generator and Discriminator both competing against each other to learn the probability distribution function, networks such as CNN, RNN, ANN etc. These traditional neural networks are easily fooled in misclassifying things by adding small amount of noise to original data, whereas GAN's are more stable and easier to train due to the amalgamation of Feed Forward Neural Network and CNN. In general, GAN's are simple Neural networks be trained in adversarial way to generate the data mimicking same distribution, Generator learns new possible sample, and the Discriminator learns how to differentiate generated samples from valid facts. Generated samples are similar in the nature but different from real distribution data. The generated samples make use of computer vision techniques such as visualization designs, realistic image generation, image classifications etc. In the proposed work, to realize the probability distribution Restricted-Boltzmann machines and Deep Belief networks are used. The performance of the GAN Networks is evaluated on various standard datasets to realize the complex tasks such as image prediction, handwritten digit's generation, clothing classification, image segmentation tasks etc. From the experimental results, it is clearly evident that the performance of GAN outperforms other state of the art classifiers on all the benchmark datasets.
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